{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Detection of probable backorders(returned orders) on e-commerce products' transactions using Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## About"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often we see that some products that are ordered from e-commerce websites get returned back to the vendor dude to various reasons like damaged goods, customer dissatisfaction etc. This creates a lot of disruption in the logistics and supply-chain management for the e-commerce service provider . If a product gets returned back, along with the obvious transportation charges, it involves a lot of planning for storing that product in the warehouses or accounting for it in the inventory etc. Hence it will prove to be very beneficial to an e-commerce service provider if such backorders could be anticipated by them and they could account for it's disruptions beforehand to maintain the smoothness of their operations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.options.mode.chained_assignment = None\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Part backorders is a common supply chain problem. Working to identify parts at risk of backorder before the event occurs so the business has time to react.\n",
    "\n",
    "Training data file contains the historical data for the 8 weeks prior to the week we are trying to predict. The data was taken as weekly snapshots at the start of each week. \n",
    "\n",
    "Columns are defined as follows:\n",
    "sku - Random ID for the product\n",
    "national_inv - Current inventory level for the part\n",
    "lead_time - Transit time for product (if available)\n",
    "in_transit_qty - Amount of product in transit from source\n",
    "forecast_3_month - Forecast sales for the next 3 months\n",
    "forecast_6_month - Forecast sales for the next 6 months\n",
    "forecast_9_month - Forecast sales for the next 9 months\n",
    "sales_1_month - Sales quantity for the prior 1 month time period\n",
    "sales_3_month - Sales quantity for the prior 3 month time period\n",
    "sales_6_month - Sales quantity for the prior 6 month time period\n",
    "sales_9_month - Sales quantity for the prior 9 month time period\n",
    "min_bank - Minimum recommend amount to stock\n",
    "potential_issue - Source issue for part identified\n",
    "pieces_past_due - Parts overdue from source\n",
    "perf_6_month_avg - Source performance for prior 6 month period\n",
    "perf_12_month_avg - Source performance for prior 12 month period\n",
    "local_bo_qty - Amount of stock orders overdue\n",
    "deck_risk - Part risk flag\n",
    "oe_constraint - Part risk flag\n",
    "ppap_risk - Part risk flag\n",
    "stop_auto_buy - Part risk flag\n",
    "rev_stop - Part risk flag\n",
    "went_on_backorder - Product actually went on backorder. This is the target value.\n",
    "\n",
    "\n",
    "The data has 1687861 rows and 23 features with a size of around 300 mb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1687861 entries, 0 to 1687860\n",
      "Data columns (total 23 columns):\n",
      "sku                  1687861 non-null object\n",
      "national_inv         1687860 non-null float64\n",
      "lead_time            1586967 non-null float64\n",
      "in_transit_qty       1687860 non-null float64\n",
      "forecast_3_month     1687860 non-null float64\n",
      "forecast_6_month     1687860 non-null float64\n",
      "forecast_9_month     1687860 non-null float64\n",
      "sales_1_month        1687860 non-null float64\n",
      "sales_3_month        1687860 non-null float64\n",
      "sales_6_month        1687860 non-null float64\n",
      "sales_9_month        1687860 non-null float64\n",
      "min_bank             1687860 non-null float64\n",
      "potential_issue      1687860 non-null object\n",
      "pieces_past_due      1687860 non-null float64\n",
      "perf_6_month_avg     1687860 non-null float64\n",
      "perf_12_month_avg    1687860 non-null float64\n",
      "local_bo_qty         1687860 non-null float64\n",
      "deck_risk            1687860 non-null object\n",
      "oe_constraint        1687860 non-null object\n",
      "ppap_risk            1687860 non-null object\n",
      "stop_auto_buy        1687860 non-null object\n",
      "rev_stop             1687860 non-null object\n",
      "went_on_backorder    1687860 non-null object\n",
      "dtypes: float64(15), object(8)\n",
      "memory usage: 296.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('../Data/Train.csv')\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sku                       0\n",
       "national_inv              1\n",
       "lead_time            100894\n",
       "in_transit_qty            1\n",
       "forecast_3_month          1\n",
       "forecast_6_month          1\n",
       "forecast_9_month          1\n",
       "sales_1_month             1\n",
       "sales_3_month             1\n",
       "sales_6_month             1\n",
       "sales_9_month             1\n",
       "min_bank                  1\n",
       "potential_issue           1\n",
       "pieces_past_due           1\n",
       "perf_6_month_avg          1\n",
       "perf_12_month_avg         1\n",
       "local_bo_qty              1\n",
       "deck_risk                 1\n",
       "oe_constraint             1\n",
       "ppap_risk                 1\n",
       "stop_auto_buy             1\n",
       "rev_stop                  1\n",
       "went_on_backorder         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Munging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we identify the impurities in the data and clean them to make it suitable for further processing.\n",
    "The techniques applied in this is rectifying missing data and converting the string fields to numeric form with binarization. Also we are normalizing the data to give us better insights as to what steps to take for a better modelling.\n",
    "\n",
    "Explaining some of the strategies used in the preprocessing:\n",
    "\n",
    "1.    Slice the dataset in half, cutting off items with no forecast or sales in the past 3 months. This strategy allows us to reduce 50% the original dataset, loosing only 300 items\n",
    "2.    Binaries were converted from strings ('Yes' and 'No') to 1 and 0.\n",
    "3.    The attributes related to quantities were normalized (std dev equal to 1) per row. Therefore, parts with different order of magnitudes are approximated. For example: 1 unit of a expensive machine may be different from 1 unit of a screw, but if we standard deviate all the quantities we have, we can get a better proportion of equivalence between those items.\n",
    "4.    Missing values for lead_time and perf_month_avg were replaced using series median and mean.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def process(df):\n",
    "    # Imput missing lines and drop line with problem\n",
    "    from sklearn.preprocessing import Imputer\n",
    "    df['lead_time'] = Imputer(strategy='median').fit_transform(\n",
    "                                    df['lead_time'].values.reshape(-1, 1))\n",
    "    df = df.dropna()\n",
    "    for col in ['perf_6_month_avg', 'perf_12_month_avg']:\n",
    "        df[col] = Imputer(missing_values=-99).fit_transform(df[col].values.reshape(-1, 1))\n",
    "    # Convert to binaries\n",
    "    for col in ['potential_issue', 'deck_risk', 'oe_constraint', 'ppap_risk',\n",
    "               'stop_auto_buy', 'rev_stop', 'went_on_backorder']:\n",
    "        df[col] = (df[col] == 'Yes').astype(int)\n",
    "    # Normalization    \n",
    "    from sklearn.preprocessing import normalize\n",
    "    qty_related = ['national_inv', 'in_transit_qty', 'forecast_3_month', \n",
    "                   'forecast_6_month', 'forecast_9_month', 'min_bank',\n",
    "                   'local_bo_qty', 'pieces_past_due', 'sales_1_month', 'sales_3_month', \n",
    "                   'sales_6_month', 'sales_9_month',]\n",
    "    df[qty_related] = normalize(df[qty_related], axis=1)\n",
    "    # Obsolete parts - optional\n",
    "    #df = df.loc[(df[\"forecast_3_month\"]>0)|(df[\"sales_9_month\"]>0)]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             sku  national_inv  lead_time  in_transit_qty  forecast_3_month  \\\n",
      "0        1026827      0.000000        8.0        0.000000          0.000000   \n",
      "1        1043384      1.000000        9.0        0.000000          0.000000   \n",
      "2        1043696      1.000000        8.0        0.000000          0.000000   \n",
      "3        1043852      0.989949        8.0        0.000000          0.000000   \n",
      "4        1044048      0.872872        8.0        0.000000          0.000000   \n",
      "5        1044198      1.000000        8.0        0.000000          0.000000   \n",
      "6        1044643      0.999993        8.0        0.000000          0.000000   \n",
      "7        1045098      1.000000        2.0        0.000000          0.000000   \n",
      "8        1045815      0.592005        8.0        0.000000          0.063429   \n",
      "9        1045867      1.000000        8.0        0.000000          0.000000   \n",
      "10       1045918      0.000000        2.0        0.000000          0.000000   \n",
      "11       1047146      1.000000        8.0        0.000000          0.000000   \n",
      "12       1047199      1.000000        8.0        0.000000          0.000000   \n",
      "13       1047661      1.000000        8.0        0.000000          0.000000   \n",
      "14       1049160      1.000000        8.0        0.000000          0.000000   \n",
      "15       1049468      1.000000        8.0        0.000000          0.000000   \n",
      "16       1050390      1.000000        2.0        0.000000          0.000000   \n",
      "17       1050440      1.000000        2.0        0.000000          0.000000   \n",
      "18       1117808      1.000000        8.0        0.000000          0.000000   \n",
      "19       1050856      1.000000        8.0        0.000000          0.000000   \n",
      "20       1118063      0.992278        8.0        0.000000          0.000000   \n",
      "21       1127154      1.000000        8.0        0.000000          0.000000   \n",
      "22       1127410      0.993884        8.0        0.000000          0.000000   \n",
      "23       1128393      0.999541        8.0        0.000000          0.000000   \n",
      "24       1051516      1.000000        8.0        0.000000          0.000000   \n",
      "25       1128591      0.999717        8.0        0.000000          0.000000   \n",
      "26       1130534      1.000000        8.0        0.000000          0.000000   \n",
      "27       1051715      0.942809        2.0        0.000000          0.000000   \n",
      "28       1053950      0.000000        8.0        0.000000          0.000000   \n",
      "29       1132770      1.000000        8.0        0.000000          0.000000   \n",
      "...          ...           ...        ...             ...               ...   \n",
      "1687830  1424742      0.135040        2.0        0.000000          0.228288   \n",
      "1687831  1411748      0.366189        4.0        0.256332          0.000000   \n",
      "1687832  1420360      0.121442        2.0        0.031779          0.179325   \n",
      "1687833  1523901      0.096979        2.0        0.038110          0.221844   \n",
      "1687834  1537492      0.062968        3.0        0.027827          0.216588   \n",
      "1687835  1588495      0.524842        8.0        0.018497          0.000000   \n",
      "1687836  1473147     -0.035348        8.0        0.009401          0.295572   \n",
      "1687837  1400108      0.157494        4.0        0.145086          0.190561   \n",
      "1687838  1383363     -0.141740        4.0        0.144466          0.237824   \n",
      "1687839  1511161      0.262074       12.0        0.020502          0.263857   \n",
      "1687840  1547033     -0.086401       12.0        0.090077          0.457739   \n",
      "1687841  1413443      0.153169        4.0        0.062336          0.153615   \n",
      "1687842  1368348      0.054388        8.0        0.034809          0.293698   \n",
      "1687843  1409190     -0.042914        3.0        0.772454          0.000000   \n",
      "1687844  1529626      0.137667        2.0        0.056703          0.203115   \n",
      "1687845  1375433      0.194070        4.0        0.095077          0.135467   \n",
      "1687846  1497988     -0.004325       12.0        0.000000          0.184535   \n",
      "1687847  1375861     -0.052735        8.0        0.000000          0.194556   \n",
      "1687848  1573563      0.122797        8.0        0.015743          0.277081   \n",
      "1687849  1460296      0.061525        2.0        0.018864          0.204553   \n",
      "1687850  1446671     -0.014627        8.0        0.000000          0.000000   \n",
      "1687851  1373539     -0.024501        9.0        0.147006          0.530856   \n",
      "1687852  1478683      0.000643        8.0        0.000000          0.310465   \n",
      "1687853  1489920      0.000000        2.0        0.000000          0.396617   \n",
      "1687854  1392420      0.058667        8.0        0.066237          0.193981   \n",
      "1687855  1407754      0.000000        2.0        0.000000          0.472456   \n",
      "1687856  1373987     -0.064550        8.0        0.000000          0.322749   \n",
      "1687857  1524346     -0.041451        9.0        0.000000          0.290159   \n",
      "1687858  1439563      0.196185        9.0        0.050628          0.123407   \n",
      "1687859  1502009      0.613222        4.0        0.000000          0.000000   \n",
      "\n",
      "         forecast_6_month  forecast_9_month  sales_1_month  sales_3_month  \\\n",
      "0                0.000000          0.000000       0.000000       0.000000   \n",
      "1                0.000000          0.000000       0.000000       0.000000   \n",
      "2                0.000000          0.000000       0.000000       0.000000   \n",
      "3                0.000000          0.000000       0.000000       0.000000   \n",
      "4                0.000000          0.000000       0.000000       0.000000   \n",
      "5                0.000000          0.000000       0.000000       0.000000   \n",
      "6                0.000000          0.000000       0.000000       0.000000   \n",
      "7                0.000000          0.000000       0.000000       0.000000   \n",
      "8                0.482061          0.642748       0.000000       0.000000   \n",
      "9                0.000000          0.000000       0.000000       0.000000   \n",
      "10               0.000000          0.000000       0.000000       0.000000   \n",
      "11               0.000000          0.000000       0.000000       0.000000   \n",
      "12               0.000000          0.000000       0.000000       0.000000   \n",
      "13               0.000000          0.000000       0.000000       0.000000   \n",
      "14               0.000000          0.000000       0.000000       0.000000   \n",
      "15               0.000000          0.000000       0.000000       0.000000   \n",
      "16               0.000000          0.000000       0.000000       0.000000   \n",
      "17               0.000000          0.000000       0.000000       0.000000   \n",
      "18               0.000000          0.000000       0.000000       0.000000   \n",
      "19               0.000000          0.000000       0.000000       0.000000   \n",
      "20               0.000000          0.000000       0.000000       0.000000   \n",
      "21               0.000000          0.000000       0.000000       0.000000   \n",
      "22               0.000000          0.000000       0.000000       0.000000   \n",
      "23               0.000000          0.000000       0.000000       0.000000   \n",
      "24               0.000000          0.000000       0.000000       0.000000   \n",
      "25               0.000000          0.000000       0.000000       0.000000   \n",
      "26               0.000000          0.000000       0.000000       0.000000   \n",
      "27               0.000000          0.000000       0.000000       0.000000   \n",
      "28               0.000000          0.000000       0.288675       0.288675   \n",
      "29               0.000000          0.000000       0.000000       0.000000   \n",
      "...                   ...               ...            ...            ...   \n",
      "1687830          0.407209          0.502285       0.052240       0.204519   \n",
      "1687831          0.000000          0.173940       0.078731       0.238023   \n",
      "1687832          0.335951          0.485767       0.077556       0.214887   \n",
      "1687833          0.463518          0.714487       0.044617       0.139117   \n",
      "1687834          0.411660          0.616773       0.070331       0.199113   \n",
      "1687835          0.000000          0.000000       0.043929       0.180342   \n",
      "1687836          0.461032          0.638149       0.114318       0.171853   \n",
      "1687837          0.354434          0.559275       0.062798       0.174115   \n",
      "1687838          0.278711          0.305969       0.322323       0.401371   \n",
      "1687839          0.263857          0.620419       0.073095       0.173824   \n",
      "1687840          0.457739          0.457739       0.000000       0.330896   \n",
      "1687841          0.342850          0.487559       0.069906       0.231980   \n",
      "1687842          0.391597          0.554762       0.095724       0.213203   \n",
      "1687843          0.000000          0.000000       0.214571       0.343313   \n",
      "1687844          0.394946          0.609346       0.069962       0.187599   \n",
      "1687845          0.292544          0.373182       0.044542       0.159861   \n",
      "1687846          0.258061          0.331587       0.139843       0.260944   \n",
      "1687847          0.293114          0.371960       0.029183       0.218363   \n",
      "1687848          0.374688          0.440810       0.053527       0.179473   \n",
      "1687849          0.409105          0.602293       0.045706       0.159051   \n",
      "1687850          0.453425          0.628945       0.043880       0.234026   \n",
      "1687851          0.530856          0.530856       0.000000       0.000000   \n",
      "1687852          0.310465          0.358674       0.015105       0.164553   \n",
      "1687853          0.579318          0.653432       0.000766       0.146314   \n",
      "1687854          0.369037          0.586673       0.060560       0.219529   \n",
      "1687855          0.472456          0.472456       0.000000       0.236228   \n",
      "1687856          0.451848          0.580948       0.064550       0.193649   \n",
      "1687857          0.373062          0.455965       0.000000       0.331611   \n",
      "1687858          0.275292          0.398698       0.110750       0.199349   \n",
      "1687859          0.000000          0.000000       0.064550       0.225924   \n",
      "\n",
      "         sales_6_month        ...          pieces_past_due  perf_6_month_avg  \\\n",
      "0             0.000000        ...                      0.0          0.782381   \n",
      "1             0.000000        ...                      0.0          0.990000   \n",
      "2             0.000000        ...                      0.0          0.782381   \n",
      "3             0.000000        ...                      0.0          0.100000   \n",
      "4             0.000000        ...                      0.0          0.782381   \n",
      "5             0.000000        ...                      0.0          0.820000   \n",
      "6             0.000000        ...                      0.0          0.782381   \n",
      "7             0.000000        ...                      0.0          0.000000   \n",
      "8             0.000000        ...                      0.0          0.782381   \n",
      "9             0.000000        ...                      0.0          0.820000   \n",
      "10            0.000000        ...                      0.0          0.910000   \n",
      "11            0.000000        ...                      0.0          0.782381   \n",
      "12            0.000000        ...                      0.0          0.782381   \n",
      "13            0.000000        ...                      0.0          0.782381   \n",
      "14            0.000000        ...                      0.0          0.782381   \n",
      "15            0.000000        ...                      0.0          0.820000   \n",
      "16            0.000000        ...                      0.0          1.000000   \n",
      "17            0.000000        ...                      0.0          1.000000   \n",
      "18            0.000000        ...                      0.0          0.782381   \n",
      "19            0.000000        ...                      0.0          0.782381   \n",
      "20            0.000000        ...                      0.0          0.782381   \n",
      "21            0.000000        ...                      0.0          0.782381   \n",
      "22            0.000000        ...                      0.0          0.782381   \n",
      "23            0.000000        ...                      0.0          0.782381   \n",
      "24            0.000000        ...                      0.0          0.782381   \n",
      "25            0.000000        ...                      0.0          0.782381   \n",
      "26            0.000000        ...                      0.0          0.782381   \n",
      "27            0.235702        ...                      0.0          0.782381   \n",
      "28            0.288675        ...                      0.0          0.782381   \n",
      "29            0.000000        ...                      0.0          0.782381   \n",
      "...                ...        ...                      ...               ...   \n",
      "1687830       0.383178        ...                      0.0          1.000000   \n",
      "1687831       0.476046        ...                      0.0          0.730000   \n",
      "1687832       0.399509        ...                      0.0          0.990000   \n",
      "1687833       0.263362        ...                      0.0          0.990000   \n",
      "1687834       0.341975        ...                      0.0          1.000000   \n",
      "1687835       0.473976        ...                      0.0          0.782381   \n",
      "1687836       0.274889        ...                      0.0          0.440000   \n",
      "1687837       0.403537        ...                      0.0          0.970000   \n",
      "1687838       0.447709        ...                      0.0          0.480000   \n",
      "1687839       0.345866        ...                      0.0          0.950000   \n",
      "1687840       0.343764        ...                      0.0          0.780000   \n",
      "1687841       0.404964        ...                      0.0          0.910000   \n",
      "1687842       0.348086        ...                      0.0          0.940000   \n",
      "1687843       0.343313        ...                      0.0          0.870000   \n",
      "1687844       0.356862        ...                      0.0          0.930000   \n",
      "1687845       0.446884        ...                      0.0          0.730000   \n",
      "1687846       0.426738        ...                      0.0          0.000000   \n",
      "1687847       0.451574        ...                      0.0          0.782381   \n",
      "1687848       0.355797        ...                      0.0          0.690000   \n",
      "1687849       0.342603        ...                      0.0          0.970000   \n",
      "1687850       0.336412        ...                      0.0          0.782381   \n",
      "1687851       0.220509        ...                      0.0          0.030000   \n",
      "1687852       0.437415        ...                      0.0          0.840000   \n",
      "1687853       0.146314        ...                      0.0          0.980000   \n",
      "1687854       0.401682        ...                      0.0          0.850000   \n",
      "1687855       0.330719        ...                      0.0          0.690000   \n",
      "1687856       0.193649        ...                      0.0          0.782381   \n",
      "1687857       0.455965        ...                      0.0          0.860000   \n",
      "1687858       0.484134        ...                      0.0          0.860000   \n",
      "1687859       0.387298        ...                      0.0          0.730000   \n",
      "\n",
      "         perf_12_month_avg  local_bo_qty  deck_risk  oe_constraint  ppap_risk  \\\n",
      "0                 0.776976      0.000000          0              0          0   \n",
      "1                 0.990000      0.000000          0              0          0   \n",
      "2                 0.776976      0.000000          1              0          0   \n",
      "3                 0.130000      0.000000          0              0          0   \n",
      "4                 0.776976      0.000000          1              0          0   \n",
      "5                 0.870000      0.000000          0              0          0   \n",
      "6                 0.776976      0.000000          1              0          0   \n",
      "7                 0.000000      0.000000          1              0          1   \n",
      "8                 0.776976      0.000000          0              0          0   \n",
      "9                 0.870000      0.000000          0              0          0   \n",
      "10                0.820000      0.000000          0              0          0   \n",
      "11                0.776976      0.000000          1              0          0   \n",
      "12                0.776976      0.000000          1              0          0   \n",
      "13                0.776976      0.000000          1              0          0   \n",
      "14                0.776976      0.000000          1              0          0   \n",
      "15                0.780000      0.000000          0              0          0   \n",
      "16                0.980000      0.000000          0              0          0   \n",
      "17                1.000000      0.000000          0              0          0   \n",
      "18                0.776976      0.000000          0              0          0   \n",
      "19                1.000000      0.000000          0              0          1   \n",
      "20                0.776976      0.000000          1              0          0   \n",
      "21                0.776976      0.000000          1              0          0   \n",
      "22                0.776976      0.000000          1              0          0   \n",
      "23                0.776976      0.000000          1              0          1   \n",
      "24                1.000000      0.000000          0              0          0   \n",
      "25                0.776976      0.000000          1              0          0   \n",
      "26                0.776976      0.000000          1              0          1   \n",
      "27                0.776976      0.000000          0              0          0   \n",
      "28                0.776976      0.000000          1              0          0   \n",
      "29                0.776976      0.000000          1              0          0   \n",
      "...                    ...           ...        ...            ...        ...   \n",
      "1687830           1.000000      0.000522          0              0          0   \n",
      "1687831           0.780000      0.027464          0              0          0   \n",
      "1687832           0.950000      0.000378          0              0          0   \n",
      "1687833           0.990000      0.000930          1              0          0   \n",
      "1687834           1.000000      0.000454          0              0          0   \n",
      "1687835           0.776976      0.002312          0              0          1   \n",
      "1687836           0.620000      0.047382          0              0          0   \n",
      "1687837           0.960000      0.000059          0              0          0   \n",
      "1687838           0.620000      0.151962          0              0          0   \n",
      "1687839           0.860000      0.000891          0              0          1   \n",
      "1687840           0.780000      0.086401          0              0          0   \n",
      "1687841           0.930000      0.002226          0              0          0   \n",
      "1687842           0.960000      0.034809          0              0          0   \n",
      "1687843           0.720000      0.042914          1              0          0   \n",
      "1687844           0.960000      0.000282          0              0          0   \n",
      "1687845           0.780000      0.025951          0              0          0   \n",
      "1687846           0.010000      0.004325          0              0          0   \n",
      "1687847           0.776976      0.052735          1              0          0   \n",
      "1687848           0.690000      0.028338          0              0          0   \n",
      "1687849           0.980000      0.000455          0              0          0   \n",
      "1687850           0.776976      0.234026          1              0          0   \n",
      "1687851           0.100000      0.171507          0              0          0   \n",
      "1687852           0.770000      0.014784          0              0          0   \n",
      "1687853           0.990000      0.000766          0              0          0   \n",
      "1687854           0.900000      0.000473          0              0          0   \n",
      "1687855           0.690000      0.236228          1              0          0   \n",
      "1687856           0.776976      0.064550          0              0          0   \n",
      "1687857           0.840000      0.041451          1              0          0   \n",
      "1687858           0.840000      0.018986          0              0          0   \n",
      "1687859           0.780000      0.032275          0              0          0   \n",
      "\n",
      "         stop_auto_buy  rev_stop  went_on_backorder  \n",
      "0                    1         0                  0  \n",
      "1                    1         0                  0  \n",
      "2                    1         0                  0  \n",
      "3                    1         0                  0  \n",
      "4                    1         0                  0  \n",
      "5                    1         0                  0  \n",
      "6                    1         0                  0  \n",
      "7                    1         0                  0  \n",
      "8                    1         0                  0  \n",
      "9                    1         0                  0  \n",
      "10                   1         0                  0  \n",
      "11                   1         0                  0  \n",
      "12                   1         0                  0  \n",
      "13                   0         0                  0  \n",
      "14                   1         0                  0  \n",
      "15                   1         0                  0  \n",
      "16                   1         0                  0  \n",
      "17                   1         0                  0  \n",
      "18                   1         0                  0  \n",
      "19                   1         0                  0  \n",
      "20                   1         0                  0  \n",
      "21                   1         0                  0  \n",
      "22                   1         0                  0  \n",
      "23                   1         0                  0  \n",
      "24                   1         0                  0  \n",
      "25                   1         0                  0  \n",
      "26                   0         0                  0  \n",
      "27                   1         0                  0  \n",
      "28                   1         0                  0  \n",
      "29                   1         0                  0  \n",
      "...                ...       ...                ...  \n",
      "1687830              1         0                  0  \n",
      "1687831              1         0                  0  \n",
      "1687832              1         0                  0  \n",
      "1687833              1         0                  0  \n",
      "1687834              1         0                  0  \n",
      "1687835              0         0                  0  \n",
      "1687836              1         0                  1  \n",
      "1687837              1         0                  0  \n",
      "1687838              1         0                  0  \n",
      "1687839              1         0                  0  \n",
      "1687840              1         0                  0  \n",
      "1687841              1         0                  0  \n",
      "1687842              1         0                  0  \n",
      "1687843              1         0                  0  \n",
      "1687844              1         0                  0  \n",
      "1687845              1         0                  0  \n",
      "1687846              1         0                  0  \n",
      "1687847              1         0                  0  \n",
      "1687848              1         0                  0  \n",
      "1687849              1         0                  0  \n",
      "1687850              0         0                  0  \n",
      "1687851              1         0                  0  \n",
      "1687852              1         0                  0  \n",
      "1687853              0         0                  1  \n",
      "1687854              1         0                  0  \n",
      "1687855              1         0                  0  \n",
      "1687856              1         0                  0  \n",
      "1687857              0         0                  1  \n",
      "1687858              1         0                  0  \n",
      "1687859              1         0                  0  \n",
      "\n",
      "[1687860 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "df = process(df)\n",
    "print df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1687860 entries, 0 to 1687859\n",
      "Data columns (total 23 columns):\n",
      "sku                  1687860 non-null object\n",
      "national_inv         1687860 non-null float64\n",
      "lead_time            1687860 non-null float64\n",
      "in_transit_qty       1687860 non-null float64\n",
      "forecast_3_month     1687860 non-null float64\n",
      "forecast_6_month     1687860 non-null float64\n",
      "forecast_9_month     1687860 non-null float64\n",
      "sales_1_month        1687860 non-null float64\n",
      "sales_3_month        1687860 non-null float64\n",
      "sales_6_month        1687860 non-null float64\n",
      "sales_9_month        1687860 non-null float64\n",
      "min_bank             1687860 non-null float64\n",
      "potential_issue      1687860 non-null int64\n",
      "pieces_past_due      1687860 non-null float64\n",
      "perf_6_month_avg     1687860 non-null float64\n",
      "perf_12_month_avg    1687860 non-null float64\n",
      "local_bo_qty         1687860 non-null float64\n",
      "deck_risk            1687860 non-null int64\n",
      "oe_constraint        1687860 non-null int64\n",
      "ppap_risk            1687860 non-null int64\n",
      "stop_auto_buy        1687860 non-null int64\n",
      "rev_stop             1687860 non-null int64\n",
      "went_on_backorder    1687860 non-null int64\n",
      "dtypes: float64(15), int64(7), object(1)\n",
      "memory usage: 309.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=range(1,23)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2911cf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_2d(X, y, title=''):\n",
    "    from sklearn.preprocessing import StandardScaler\n",
    "    X_std = StandardScaler().fit_transform(X)\n",
    "\n",
    "    from sklearn.decomposition import PCA\n",
    "    dec = PCA(n_components=2)\n",
    "    X_reduced = dec.fit_transform(X_std)\n",
    "    \n",
    "    f, ax = plt.subplots(figsize=(6,6))\n",
    "    ax.scatter(X_reduced[y==0,0], X_reduced[y==0,1], alpha=0.5, \n",
    "               facecolors='none', edgecolors='cornflowerblue', label=\"Negative\")\n",
    "    ax.scatter(X_reduced[y==1,0], X_reduced[y==1,1], c='darkorange', marker='*', \n",
    "               label='Positive')\n",
    "    plt.title(\"Explained variance ratio: %.2f%%\" % (100*dec.explained_variance_ratio_.sum()))\n",
    "    ax.legend(loc='lower left')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.set_xlabel('PC1')\n",
    "    ax.set_ylabel('PC2')\n",
    "    plt.show()\n",
    "    \n",
    "sample = df.sample(5000, random_state=36)\n",
    "\n",
    "X_sample = sample.drop('went_on_backorder',axis=1).values\n",
    "y_sample = sample['went_on_backorder'].values\n",
    "\n",
    "plot_2d(X_sample, y_sample)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above figure we have found out the explained variance by plotting a sample of the dataset.\n",
    "To provide a basic understanding about the problem, we used PCA to reduce the dataset to 2 components, and plot according to the class - black stars represent the minority or positive class (items which went on backorder), whilst empty circles represent majority or negative class (other items)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imbalanced ratio in training set: 1:148\n"
     ]
    }
   ],
   "source": [
    "X = df.drop('went_on_backorder', axis=1).values\n",
    "y = df['went_on_backorder'].values\n",
    "print('Imbalanced ratio in training set: 1:%i' % (Counter(y)[0]/Counter(y)[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the imbalance is quite large for this data with the minority class being almost 0.6% of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will be treating the Imbalance and fit different models to our data\n",
    "We adopted here 3 estimators. Note that we do not use cross-validation or parameter tuning yet.\n",
    "\n",
    "1.    DecisionTree (CART): This is our base estimator of all methods. Decision trees present great results in imbalanced domains, since it leafs are generated using 'gini' or 'entropy', not 'accuracy' as general learning algorithms.\n",
    "2.    RandomUnderSampling (RUS): One of the most common sampling techniques. Rexamples the dataset by eliminating random instances of majority class.\n",
    "3.    RandomForest (FOREST): Bagging-based ensemble.\n",
    "4. GradientBoosting (GBOOST): Boosting-based ensemble.\n",
    "5. UnderBagging - A different model, not currently implemented in sklearn. It works as a bagging of emphasized textundersampled sets. We used the sklearn.ensemble.BaggingClassifier and imblearn.ensemble.RandomUnderSampler to reproduce the UnderBagging model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, stratify=y, random_state=42)\n",
    "\n",
    "from sklearn import tree, ensemble\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import make_pipeline\n",
    "\n",
    "cart = tree.DecisionTreeClassifier(criterion='entropy', max_depth=8, min_samples_leaf=5)\n",
    "rus = make_pipeline(RandomUnderSampler(),tree.DecisionTreeClassifier(criterion='entropy', max_depth=8, min_samples_leaf=5))\n",
    "forest = ensemble.RandomForestClassifier(criterion='entropy', max_depth=15, min_samples_leaf=5)\n",
    "gboost = ensemble.GradientBoostingClassifier(max_depth=15, min_samples_leaf=5)\n",
    "\n",
    "cart.fit(X_train, y_train)\n",
    "rus.fit(X_train, y_train)\n",
    "forest.fit(X_train, y_train)\n",
    "\n",
    "n_splits = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import BaggingClassifier, AdaBoostClassifier\n",
    "ub = BaggingClassifier(warm_start=True, n_estimators=0)\n",
    "\n",
    "for split in range(n_splits):\n",
    "    X_res, y_res = RandomUnderSampler(random_state=split).fit_sample(X_train,y_train) \n",
    "    ub.n_estimators += 1\n",
    "    ub.fit(X_res, y_res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visual Analysis and Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2891b890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def roc_auc_plot(y_true, y_proba, label=' ', l='-', lw=1.0):\n",
    "    from sklearn.metrics import roc_curve, roc_auc_score\n",
    "    fpr, tpr, _ = roc_curve(y_true, y_proba[:,1])\n",
    "    ax.plot(fpr, tpr, linestyle=l, linewidth=lw,\n",
    "            label=\"%s (area=%.3f)\"%(label,roc_auc_score(y_true, y_proba[:,1])))\n",
    "\n",
    "f, ax = plt.subplots(figsize=(6,6))\n",
    "\n",
    "roc_auc_plot(y_test,ub.predict_proba(X_test),label='UB ',l='-')\n",
    "roc_auc_plot(y_test,forest.predict_proba(X_test),label='FOREST ',l='--')\n",
    "roc_auc_plot(y_test,cart.predict_proba(X_test),label='CART', l='-.')\n",
    "roc_auc_plot(y_test,rus.predict_proba(X_test),label='RUS',l=':')\n",
    "\n",
    "ax.plot([0,1], [0,1], color='k', linewidth=0.5, linestyle='--', \n",
    "        label='Random Classifier')    \n",
    "ax.legend(loc=\"lower right\")    \n",
    "ax.set_xlabel('False Positive Rate')\n",
    "ax.set_ylabel('True Positive Rate')\n",
    "ax.set_xlim([0, 1])\n",
    "ax.set_ylim([0, 1])\n",
    "ax.set_title('Receiver Operator Characteristic curves')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is the AUC-ROC plot for the different models fitted to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x29227510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def precision_recall_plot(y_true, y_proba, label=' ', l='-', lw=1.0):\n",
    "    from sklearn.metrics import precision_recall_curve, average_precision_score\n",
    "    precision, recall, _ = precision_recall_curve(y_test,\n",
    "                                                  y_proba[:,1])\n",
    "    average_precision = average_precision_score(y_test, y_proba[:,1],\n",
    "                                                     average=\"micro\")\n",
    "    ax.plot(recall, precision, label='%s (average=%.3f)'%(label,average_precision),\n",
    "            linestyle=l, linewidth=lw)\n",
    "\n",
    "f, ax = plt.subplots(figsize=(6,6))\n",
    "precision_recall_plot(y_test,ub.predict_proba(X_test),label='UB ',l='-')\n",
    "precision_recall_plot(y_test,forest.predict_proba(X_test),label='FOREST ',l='-')\n",
    "precision_recall_plot(y_test,cart.predict_proba(X_test),label='CART',l='-.')\n",
    "precision_recall_plot(y_test,rus.predict_proba(X_test),label='RUS',l=':')\n",
    "\n",
    "ax.set_xlabel('Recall')\n",
    "ax.set_ylabel('Precision')\n",
    "ax.legend(loc=\"upper right\")\n",
    "ax.grid(True)\n",
    "ax.set_xlim([0, 1])\n",
    "ax.set_ylim([0, 1])\n",
    "ax.set_title('Precision-recall curves')\n",
    "sns.despine()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above is the Precision-Recall plot for the same models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ensembles and sampling can easily be combined to perform best results. There are several other models can be applied, i.e. RUSBoost, SMOTEBoost, OverBagging, SMOTEBagging.\n",
    "\n",
    "The best working model here is UnderBagging, because in the given dataset, which presents almost 1.8 millions of majority examples case, undersampling greatly increases the computational cost of model fitting.\n",
    "\n",
    "The CART model, as a weak and soft classifier can be a useful base_classifier. Although, others classifiers can be used:  SVM, KNN, RandomForests, LDA, among others.\n",
    "\n"
   ]
  }
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